(Adapted from the application) The Population Aging Research Center (PARC) in the Population Studies Center (PSC) at University of Pennsylvania (UP) is well-positioned to solidify and expand its program in the demography and economics of health and aging by building on its substantial and rapidly growing strengths in these areas. The PSC has devoted increasing attention to issues related to the demography and economics of health and aging. The research on aging has coalesced into a strong and expanding program. The number of PARC Research Associates engaged in research on aging has increased substantially during the period of P20 support and will continue to increase during the P30 period. The specific aims of PARC for 1999-2004 are to: (1) Provide general support to strengthen research on the PARC scientific themes: (a) Mortality and Health at Old Ages, (b) Economics of Pensions, Retirement, Work and Health, and (c) Aging in Families and Households and Intergenerational Relations, with the cross-cutting theme of the Diversity of Aging. (2) Provide support for the PARC Administrative and Research Support Core to (a) support PARC planning, coordination, review and management and (b) provide shared support services to facilitate research on aging. (3) Provide support for expanding the Program Development Core activities, including (a) increased exploratory, pilot projects, selected by a competitive review and (b) intensified new faculty development in aging topics. (4) Strengthen the External Innovative Network Core through (a) Inter-University Workshops on New Research Areas and (b) involving demographic researchers in the symposia organized by the Pension Research Council. (5) Strengthen the External Research Resources Support and Dissemination Core that will (a) electronically disseminate PARC Working Papers and information about new data sets and methodologies developed at PARC and (b) develop and distribute periodic Penn-PARC Policy Bulletins with synthesis of PARC research results targeted at the scientific and policy-making communities. (6) Develop a Statistical Data Enclave Core to support analysis of large-scale, often- longitudinal, databases with linked administrative data, geocoding, and, potentially, genetic data with safeguards for data security/confidentiality.